{
 "metadata": {
  "name": "",
  "signature": "sha256:cc9cbad4a934326ed63ea87f9e4e0a733fa14c59f15868c7c250cb74e8e642ec"
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = np.genfromtxt('Advertising.csv', delimiter=\",\")\n",
      "x_data = data[1:,1:4]\n",
      "y_data = data[1:,4]\n",
      "print (len(x_data[0]))\n",
      "print (len(y_data))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3\n",
        "200\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def compute_loss(b, k, x_data, y_data):\n",
      "    loss = 0\n",
      "    for i in range(len(x_data)):\n",
      "        z = 0\n",
      "        for j in range(len(k)):\n",
      "            z += k[j]*x_data[i][j]\n",
      "        loss += (y_data[i] - (z+b))**2\n",
      "    return loss/float(len(x_data)) / 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def gradient_discent(x_data, y_data, lr, epoch, k, b):\n",
      "    batch = len(x_data)\n",
      "    for i in range(epoch):\n",
      "        dz = 0\n",
      "        dk = [0 for _ in range(len(k))]\n",
      "        db = 0\n",
      "        for j in range(batch):\n",
      "            z = 0\n",
      "            for n in range(len(k)):\n",
      "                z += k[n]*x_data[j][n]\n",
      "            dz = -(y_data[j] - (z+b))\n",
      "            for n in range(len(k)):\n",
      "                dk[n] += x_data[j][n] * dz / float(batch)\n",
      "            db += dz / float(batch)\n",
      "        for n in range(len(k)):\n",
      "            k[n] -= lr[n] * dk[n]\n",
      "        b -= lr[0] * db\n",
      "    return k, b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = 0\n",
      "k = [0 for _ in range(len(x_data[0]))]\n",
      "epoch = 100\n",
      "lr = [0.000001 for _ in range(len(x_data[0]))]\n",
      "print (\"Start b = {0}, k = {1}, loss = {2}\".format(b, k, compute_loss(b, k, x_data, y_data)))\n",
      "print (\"Running.......\")\n",
      "k, b = gradient_discent(x_data, y_data, lr, epoch, k, b)\n",
      "print (\"After {3} epochs: b = {0}, k = {1}, loss = {2}\".format(b, k, compute_loss(b, k, x_data, y_data), epoch))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Start b = 0, k = [0, 0, 0], loss = 111.85812500000002\n",
        "Running.......\n",
        "After 100 epochs: b = 0.0005451538568089267, k = [0.075061049374910649, 0.016388151225817858, 0.017995224780363209], loss = 9.31831976939617"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = np.genfromtxt('Advertising.csv', delimiter=\",\")\n",
      "x_data = data[1:,2:4]\n",
      "y_data = data[1:,4]\n",
      "print (len(x_data[0]))\n",
      "print (len(y_data))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2\n",
        "200\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = 0\n",
      "k = [0 for _ in range(len(x_data[0]))]\n",
      "epoch = 400\n",
      "lr = [0.001 for _ in range(len(x_data[0]))]\n",
      "print (\"Start b = {0}, k = {1}, loss = {2}\".format(b, k, compute_loss(b, k, x_data, y_data)))\n",
      "print (\"Running.......\")\n",
      "k, b = gradient_discent(x_data, y_data, lr, epoch, k, b)\n",
      "print (\"After {3} epochs: b = {0}, k = {1}, loss = {2}\".format(b, k, compute_loss(b, k, x_data, y_data), epoch))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Start b = 0, k = [0, 0], loss = 111.85812500000002\n",
        "Running.......\n",
        "After 400 epochs: b = 0.824601004204622, k = [0.36047299862109966, 0.093764757850761909], loss = 17.183348403625363"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from mpl_toolkits.mplot3d import Axes3D\n",
      "ax = plt.figure().add_subplot(111, projection='3d')\n",
      "ax.scatter(x_data[:,0], x_data[:,1], y_data, c='r', marker='o', s=100)\n",
      "x0 = x_data[:,0]\n",
      "x1 = x_data[:,1]\n",
      "x0,x1 = np.meshgrid(x0,x1)\n",
      "z = k[0] * x0 + k[1] * x1 + b\n",
      "ax.plot_surface(x0,x1,z)\n",
      "ax.set_xlabel('x0')\n",
      "ax.set_ylabel('x1')\n",
      "ax.set_zlabel('z')\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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XYyUuvmKzbE3TOHPmDJOTk2zYsKFoE9Nqj2G5tDe7S4hhGFkhXomxSwlZ5GaO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\n+ygsLOS9994jJSXlKF6VgaMph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       "text": [
        "<matplotlib.figure.Figure at 0x7fe7f1a55358>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}